{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = [0, 10, 20, 30, 40, 50, 60, 70]\n",
    "x = [25.05, 26.51, 29.33, 31.07, 32.8, 35.05, 36.72, 40.05]\n",
    "#x = [(num+125)/5+round(random.normalvariate(0,0.5),2) for num in y]\n",
    "y1=range(0,75,1)\n",
    "x1=[(num+125)/5 for num in y1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32993 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20811 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 23450 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24459 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 23454 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39564 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24377 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31783 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 38271 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24230 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 65288 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 65289 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30749 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30721 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 37325 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 37327 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24377 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31783 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 38271 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24230 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 65288 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 65289 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30749 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30721 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 37325 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 37327 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32993 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20811 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 23450 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24459 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 23454 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\pytorch\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39564 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,9))\n",
    "plt.xlim(24,41)\n",
    "plt.scatter(x,y,100)\n",
    "plt.xlabel('弹簧长度（cm）',fontsize=18)\n",
    "plt.ylabel('砝码重量（g）',fontsize=18)\n",
    "plt.title('胡克定律实验',fontsize=20)\n",
    "plt.plot((x[0],x[7]),(y[0],y[7]),color='r')\n",
    "plt.plot((x[1],x[6]),(y[1],y[6]),color='b')\n",
    "plt.plot(x1,y1, linestyle='-.')\n",
    "plt.plot(x1,y1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8100215335409242"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.normalvariate(0,0.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.23"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(1.23456,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[25.05, 26.51, 29.33, 31.07, 32.8, 35.05, 36.72, 40.05]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AKhelHACA0gSDSnz7VjV327Tjogw1atHWdyIAMSjJdwAAACLaf59UzbUfKfiTv+nkfj/xnQZAjGKkHACAEiyeOV3BT/+swClXK6H/rb7jAIhhlHIAAIqzd6M6zLxT6xNaK/fCJ7lAEIAqxfQVAACOVpArTRuq6olOLUa9pmq16/pOBCDGMVIOAMBRFo5Jl7LnS1c+r2rNOvmOAyAOUMoBADjC7Df+oe7b39asljdKnS/xHQdAnKCUAwAQsnLBl+q+6M9aUq2n+ox4wnccAHGEUg4AgKS9O7eq1lsjtMfqqeWoqUpM4m1XAMKHUg4AiHuBwkJtePEGNXR7dODysarfuLnvSADiDKUcABD3Zk+4V6fmztWCbg+qY8+zfMcBEIco5QCAuLbgk6kasGmcZte/RH2vudt3HABxilIOAIhbB7as0Alf3a2ViR10avoY33EAxDHexQIAiE/5h1R7+ggVpKSo1uApSq1e03ciAHGMkXIAQNxxwaD2T7tN2v6tkgeOU/O2XCAIgF+UcgBA3Jn/xmOqs+otbe71S6nDj33HAQBKOQAgzmzIVM9lj2p9ozPV/JIHfKcBAEmUcgBAHNmzbaPctGGyuq3V9qaXlZCY6DsSAEiilAMA4kRBfp62jB2k/IN7FBz4slS9nu9IAPB/KOUAgLgwd+yd6pK/WIt7/VEJzbv5jgMAP0ApBwDEvKz3xqr/tqnKbHyN0i67pUqfa/2uQ/rNW4s1Z91uzVqzS11//5F+89Zird91qEqfF0B0o5QDAGLaumVZ6jL711qe3EU9Rz1Tpc/1+YrtuvAfX+qV2RsVCDpJ0sG8Qr0ye6Mu/MeX+nzF9ip9fgDRy1spN7PWZva5mS0zs6VmdmdoeQMzm2FmK0Of65ew/YVmtsLMVpnZ/eFNDwCIBgf27VbitKHKsepqOGKqUqqlVtlzrd91SLdNmqfDBQEVhgr59wqDTocLArpt0jxGzAEUy+dIeaGkXzrnTpbUX9LtZtZF0v2SPnXOdZT0aej+D5hZoqRnJF0kqYukQaFtAQCQVHSBoFUZQ9U8uFXbf/K8GrdoV6XPN+bLNSoIBEtdpyAQ1Itfrq3SHACik7dS7pzb4pybF7p9QNIySS0lXS5pYmi1iZKuKGbzvpJWOefWOOfyJb0S2g4AAElS5qTfq+ehr5R10i/UZcBFVf58b83P/p8R8qMVBp2mz99c5VkARJ8k3wEkyczaSeopKVNSU+fcFqmouJtZk2I2aSlp4xH3N0nqV8K+0yWlS1KbNm0qMTUAIFK51f9R3zVPa26ts9Vv0G/C8pyH8gp/cP/bJicUv15+YbHLAcQ376XczGpJekPSXc65/WZWps2KWVbs8IRzLkNShiSlpaWVPoQBAIh++zbJ3hgpNeqobiNfliWE54/CNasl6eARxfyP56UXv16K91+9ACKQ17OvmFmyigr5ZOfcm6HF28yseejx5pKKe6v6Jkmtj7jfSlJ2VWYFAES+vNwcrX/uGgUL8mTXT1ZKjTphe+4rerZQUkLpA0tJCaYre7YMUyIA0cTn2VdM0lhJy5xzfz/ioXckDQ/dHi7p7WI2nyOpo5m1N7MUSdeHtgMAxLHD7/xKbXOXaUHaw1KjjmF97tFnnKDkxNJ/rSYnJmjUGe3DlAhANPE5Un66pKGSzjWzBaGPiyU9LOl8M1sp6fzQfZlZCzN7X5Kcc4WS7pD0kYreIDrNObfUxxcBAIgQ8yer3rcvK6//z9XrJ0PD/vRtG9bUs0N6qXpy4v+MmCclmKonJ+rZIb3UtmHNsGcDEPnMufiZZp2WluaysrJ8xwAAVLJVC/+rdm9fqcQ2/WRDp0uJ/uZtr991SC9+uVbT52/WofxC1UxJ0pU9W2rUGe0p5ECcMLO5zrm0cm1DKQcARLN9u7bp0NNnKNEVKvX2L1W3MXO2AfhVkVLu9Y2eAAAcj2AgoPVjBqtRcJf2/nQshRxA1KKUAwCiVuaE+3Rq7hzNP+XX6tT7HN9xAKDCKOUAgKi08LNXNGDjGM2pd5H6XnO37zgAcFwo5QCAqLN5zTK1n3m3VieeoG7pL4btAkEAUFV4FQMARJXDhw4od/IgOUmpg6cqtUYt35EA4LhRygEA0cM5fTd2lNoXrtO6s55SyxM6+04EAJWCUg4AiB5ZY9V994f6ttNt6n7Otb7TAECloZQDAKLCtqVfyn1wv9TxAnW9/s++4wBApaKUAwAi3sFd2bLXhml3UmPpqgyJN3YCiDG8qgEAIlugULX+na4GCYe056fjpOr1fScCgEpHKQcARLScD38nrftSSZc9pQ7dBviOAwBVglIOAIhY8z6coBpzntGOk4dKPQb5jgMAVYZSDgCISOtXzFenb+7T8qTOqnPFY77jAECVopQDACLOwf17pFeHKNeqqf6NU1StWnXfkQCgSlHKAQARxQWDWpExXC0D2dpy/rNq2upE35EAoMpRygEAESVzyh/V++AXmtPh5+p6+qW+4wBAWFDKAQARY+l/31Payqc0r+aZ6j/4977jAEDYUMoBABFh2+Y1ajbjVm1ObKGTbn5JxgWCAMSRJN8BAABQYb5S3xypJOXpwMC3VKsOFwgCEF8o5QAA/z5+UHV3zVfelePVrnMv32kAIOz42yAAwKu5/35Omp0hnfYzVet+le84AOAFpRwA4I3bskhd5/1eS1NOVfBc3tgJIH4xfQUA4MfhPbJpQ5VSq4FaD3tFCUnJvhMBgDeMlAMAwi4YCGh1xhC5fZtlA19SncYtfUcCAK8o5QCAsJv90gM6cc9Xmnvyr6TWfX3HAQDvKOUAgLBa9Pnr6rvuBc2pe4F6X32P7zgAEBEo5QCAsMleu1xtv/i51iW2Vdf0cVwgCABCeDUEAIRFbs5B5Uy6QSanakOmqHrN2r4jAUDEoJQDAKqcCwa1KGO0OgRWa80ZT6rlCaf4jgQAEYVSDgCocnPefFJ9976vb1qPUo8fX+87DgBEHEo5AKBKbVg0Uz0W/1WLUvuo7/BHfMcBgIhEKQcAVJ1DO9X6k1uVW72J2oyarMQkrlkHAMXh1REAUCUChYUKvHqjUg7tUJ2bPpYaNfUdCQAiFiPlAIAqkTX+l0rZ8KUOnPeo1KKH7zgAENEo5QCAyrfsXfXbPEGLm12p2gNu9J0GACIepRwAUKkOZi+Tpt8iteilbqNe8B0HAKICpRwAUGkOHdirHS8OVE4wURr4kpRUzXckAIgKlHIAQKVwwaCWZ9yoNoGNWn3WP6V6rX1HAoCoQSkHAFSKzFf+ot4HPtfsE25XtzMu9x0HAKIKpRwAcNy+nfWh0lb8XfNrnK7+Q//kOw4ARB1KOQDguOzMXq8mH96sLQnNdGL6y7IEfrUAQHnxygkAqLD8vDztGH+9arjDCgx8WXXqNfQdCQCiEqUcAFBh8168QycXfKtlff+idien+Y4DAFGLUg4AqJDcea+q/45pmtXkOvW+ZLTvOAAQ1ZJ8BwAARKFtS5X6wV0KtB6g3kP/5TsNAEQ9RsoBAOWyf+8u7R43UK5aHSUOnKjkFC4QBADHi1IOACi7YFA5r45W7dwtWnXW01Ltpr4TAUBMoJQDAMruq7+r2ZZPlXPOH9Sxz/m+0wBAzKCUAwDKZMnM6XKf/Vnqeo3qnnWH7zgAEFMo5QCAY8pet0ItP/uZ1iW0UeDSpyQz35EAIKZQygEApco9fEiHXh6sJFeopBsmKzG1lu9IABBzvJ0S0czGSbpU0nbnXNfQslcldQqtUk/SXudcj2K2XSfpgKSApELnHFesAIAqsjDjZvULrNT8059Rzw7dfMcBgJjk8zzlEyQ9Leml7xc45677/raZPSFpXynbn+Oc21ll6QAAmv3GP9Rvz7/1TcsbNeCCIb7jAEDM8lbKnXMzzaxdcY+ZmUkaKOncsIYCAPyf7+bNVPdFf9bi1F7qO+IJ33EAIKZF6pzyMyRtc86tLOFxJ+ljM5trZuml7cjM0s0sy8yyduzYUelBASAW7dmxRXXeGak9Vk+tRk1RYhIXgAaAqhSppXyQpKmlPH66c66XpIsk3W5mZ5a0onMuwzmX5pxLa9y4cWXnBICY4wKF2jR2sBq4PTpw+TjVb9zcdyQAiHkRV8rNLEnSVZJeLWkd51x26PN2SdMl9Q1POgCIffbFw+qWO1dLevxWHXuWOOYBAKhEEVfKJZ0nablzblNxD5pZTTOr/f1tSRdIWhLGfAAQsw4ufEea+ZjUc6h6XXmX7zgAEDe8lXIzmyrpG0mdzGyTmd0Ueuh6HTV1xcxamNn7obtNJX1lZgslzZb0nnPuw3DlBoBYtXn1Yrnp6dpdt4t08eO+4wBAXPF59pVBJSy/sZhl2ZIuDt1eI6l7lYYDgHiTf0jNP0xXbmKyDl41QUpO9Z0IAOIKb6cHgDjngkEF3/65EncsU40hb6hG207H3ggAUKkicU45ACCMMl99WIlLX9ehH90vdfix7zgAEJco5QAQx5Znfqzeyx/XghoDVP2ce33HAYC4RSkHgDi1c+sGNfwgXdsSmqj96ElKSEz0HQkA4halHADiUEF+nraPvUG13CEVXDNRdes38h0JAOIapRwA4lDW2DvVpWCxlvb+k9qf0s93HACIe5RyAIgzc997UQO2TVVm42uUdtktvuMAAEQpB4C4sm5Zlk6e/YCWJ3dRz1HP+I4DAAihlANAvMjdrzpvj1COVVfDEVOVUo0LBAFApODiQQAQD5yT3rpV9fM2a+91b6h+i3a+EwEAjsBIOQDEgYWvPiQtf1d2wZ9U/+RzfMcBAByFUg4AMS53xafquvyfWlDnHKn/bb7jAACKwfQVAIhl+zYp9e3RKmzYUR1vnCCZ+U4EACgGI+UAEKPycnO0Y9x1coX5Sho0WTVr1/MdCQBQAko5AMSoBWNuVeN9S/TdaY9KjTr6jgMAKAWlHABi0Ozp/1K/XW/pm+bD1OnsG3zHAQAcA6UcAGLMqoVfqfuCP2hJtR7qM/IJ33EAAGVAKQeAGLJv1zbVeGuE9lodtbhpipKSU3xHAgCUAaUcAGJEIBDQ+jGD1Si4W/suG6cGTVr6jgQAKCNKOQDEiNkTfqVTc+doftdf66ReZ/uOAwAoB0o5AMSARZ+9ogEbX9Scehep79V3+44DACinY148yMwCFdy3SQo657hAEQBUpd1r1DXzV8qufpK6pb8oS2C8BQCiTVkKsznnKvQKb2bBimwHACib3JwDqvbqUCWYqUX6a1KNWr4jAQAqoCxl2x3H/o9nWwBAaZzT4hdGym1bqsCVY6T67XwnAgBUEH/jBIBoNedF9dn3sbLapSux0wW+0wAAjgPzvQEgChWun6WkD38tdbxAfQc97DsOAOA4UcoBIMrs3LpRbsIg1ajRTDWvypB4YycARD1eyQEgihQW5GvbuBtUO3hAWy98Uape33ckAEAlKNNIuZmZik5xWB7lXR8AcAxZY+9S//xFmtPzr+rTrb/vOACASlKmUyJKKqzAvk2cfQUAKs28D8ar/9bJymx0lfpdcbvvOACASnTMUl7Rc5QDACrP+uXz1GnW/VqR3Fk9Rz/nOw4AoJJRuAEgwh3cv0eaNlS5Vk31bpyilGqpviMBACpZuc6+Yma/O8YqTtJhSRsk/cc5t72iwQAAkgsG9V3GMJ0ayNaKCybplFYn+o4EAKgC5T0l4kP6//PEj34j59HLC8zscefcgxXMBgBxb9eMv6vXwZma1fEu9T/9Et9xAABVpLzTV7pKmifpG0nXSeoR+rhe0ixJWZL6S7o2dPt+M7u5krICQHxZO1ONZv1FOSdeon43/N53GgBAFSpvKR8tKVfSWc6515xzi0If0ySdJalA0vXOuTdC9xdLopQDQDlt27RauVOHyzXsoBoDX5BxgSAAiGnlfZW/XtI051zg6Aecc4WSpkkadNT9TscbEgDiSmG+CqcOUyAvR7suGStVq+07EQCgipW3lNcNfZT18Z3iXOUAUD4fPaCWh5Zo7wX/UKP2p/pOAwAIg/KW8oWSbjOztkc/YGbtJN0macERiztJ2lLRcAAQb779IEOaM0Y67Wdqefog33EAAGFS3rOv3C/pI0nLzOwtSd+FlneSdLmKSv4gSTKzapIGS3q3UpICQIxbvehrnTDrAX1Xo4dO+vFDvuMAAMKoXKXcOfeFmZ0n6e8qml9+pCxJ9zjnZobWzQuNqBdUSlIAiGH7du9Q6vQbtd9qq8HwSVJiecdMAADRrNyv+s65ryT1NbMmktqr6Lzka4q7UJBzLu/4IwJAbAsGAlo7ZrC6BHdqzaXT1LlZa9+RAABhVuGhmFAJ54qdAHCcMic+oAGHM5XZ5QH163Oe7zgAAA+OWcrNbE0F922Sgs45rgkNACVY+Plr6rf+Bc2pd4H6Xnuv7zgAAE/KMlLeTkXTVMrLJK2uwHYAEBey1y5Xuy/u1NrEduqaPo4LBAFAHCtLKXfOufUV2bmZVWQzAIh5uTkHlTPpBtWSlDpksqrX5AJBABDPGJYBgHBzTtU+ulcdAqu15oy/q+UJp/hOBADwjFIOAGFWMGecbOEU6az71OPHR59dFgAQjyjlABBGK+Z+Lvf+r7S/1dnSWff7jgMAiBCUcgAIl0M7deLnt2pfUiO5KzMk3tgJAAjhknEAEAbBwgLZ6yOUlLtHjW/6WGrY1HckAEAEKUspNzPbUIF9c+oVAAjJHHe3BmTPVOCyZ5TYvLvvOACACFOWUl6Rc5Qfk5mNk3SppO3Oua6hZQ9JGi1pR2i1B5xz7xez7YWSnpKUKOlF59zDVZERACrDvI9e1oDsl5TZ8Ar16zXEdxwAQAQqy4RGd5wfJZkg6cJilj/pnOsR+iiukCdKekbSRZK6SBpkZl3K8HUAQNht+G6BTvr6Xn2XdJJ6jH7OdxwAQIQqy0j5WknrVbHpKK1Leg7n3Ewza1eBffaVtMo5t0aSzOwVSZdL+rYC+wKAKnPowF4FXxmiAktWneFTVS21hu9IAIAIVaY55c65EyqyczMLVmCzO8xsmKQsSb90zu056vGWkjYecX+TpH6lZEiXlC5Jbdq0qUAcACg/FwxqecaN6hHYpGXnTVTX1h18RwIARLCyTl+pqPJu+5ykEyX1kLRF0hPFrFPciH2Jz+Ocy3DOpTnn0ho3blzOOABQMZmv/EW9D3yu2Sf+TF3PuNx3HABAhIuok+Q657Y55wLOuaCkMSqaqnK0TSqaFvO9VpKyw5EPAMri228+UNqKv2t+zR+p/5A/+I4DAIgCEVXKzaz5EXevlLSkmNXmSOpoZu3NLEXS9ZLeCUc+ADim/VvUYsat2pLQTB3SX5ZxgSAAQBl4u3iQmU2VdLakRma2SdLvJZ1tZj1UNB1lnaSbQ+u2UNGpDy92zhWa2R2SPlLRKRHHOeeWhv8rAICjFOZLrw1X3cR8HR44XbXrNvCdCAAQJbyVcufcoGIWjy1h3WxJFx9x/31J/3O6RADwaeXkX6jjxkzZNePUvGNP33EAAFGkrFf0bK/ynxKRK3oCiBtbv3pZHddO0oJWg9Wj69W+4wAAokxZSvkGSZ9VcP8bKrgdAESPbUvV7Itf6WCzfjpl2JO+0wAAotAxS7lzrl0YcgBAVNq3Z6dSJg1S9Wp1VGvwy1JKNd+RAABR6JilPHQhn4pyzrmXj2N7AIhYwUBAa8cM0SmHNmnXdW+pYe2mviMBAKJUWaavTAh9VGSO+DBJlHIAMSnz5d9oQM43yjz5PvXrcqbvOACAKFams68450ZWZOdmNrwi2wFApFv8xZvqt/Y5ZdU9T30H3u87DgAgypXlqhYlXsK+ircFgIiUvW6FWn/+M61PbKsu6eO4QBAA4LjxmwQAyiH38CEdevkGJSio5Bsmq0atur4jAQBiAKUcAMphUUa6OgZWac3pT6hVh66+4wAAYgSlHADKaPYbT6rvnnc1q9UI9Tj/Bt9xAAAxhFIOAGWxea7Slv5Vy2v2UZ8bH/edBgAQY8py9hUzs4qcfaUip1AEgIhTeGCHkqYNV0LtZup88zQpqUwnrgIAoMzK8pvlJUlnVHD/L1VwOwCICIHCQi17ZqBOztumpFEfSTUa+I4EAIhBxyzlzrkbw5ADACLT539Rt9x5mt3tD+rbspfvNACAGHXMUm5m445j/845d9NxbA8A3rhl7yrxv3+Xeg1T38vu8h0HABDDyjJ95UZJIyqwb5M0VhKlHEDU2bhqsRpMG63Ext2VetFjvuMAAGJcmd6t5JybWJGdH+coOwB4kXNwnwqnDFa+S1TupWPUPDnVdyQAQIwryykR3XHs/3i2BYCwc8Ggvn1hhNoGNmjTuf9S87adfEcCAMQBzlMOAEfIfPVvSjvwqWa3v03dzrzSdxwAQJyglANAyLLMj9R7+ROaX+M09R36J99xAABxhFIOAJJ2Zq9X4w/StS2hiU5In6SExETfkQAAcYRSDiDuFeTnafv4G1TDHVbBNRNVt15D35EAAHGmLGdfMTOryJU5LfQBABFt07R71aVgibL6PKa0U/r5jgMAiENlKeUjVPGzqMyo4HYAEB6LX1f7VRO1o8sIpV2a7jsNACBOlaWUP6SiUl6RUe+gpIqMsgNAlVu/LEut375DCa37q/FVj/qOAwCIY2Up5W0ltVP5S7lJWl3eQAAQFrn7lPzGcO0pTFWdq8crOSnFdyIAQBwrSyl3zrkNFdm5GVPKAUSgYFB66zY1D2zR2ktfUcN6LXwnAgDEOc6+AiDurHvnr9Lyd2UX/FknpF3gOw4AAJRyAPFl8cy31Xr+4/qu0flS/1t9xwEAQBKlHEAc2bphpVp9doc2JrZWy+EvSkyxAwBECEo5gLiQl3tI+1+6QcmuQImDJqlm7Xq+IwEA8H8o5QDiwoKMW3RS4Xf67rTH1Lpjd99xAAD4gbJe0bMiZ1/h78IAIsKcN59Sv93v6JsWwzTgJ0N9xwEA4H+UpZS3r/IUAFBFVi38Sqcu/JOWpPZQnxFP+I4DAECxjlnKnXPrwxEEACrb3p1bVfOtG7XH6qrFTVOUlMwFggAAkYk55QBiUzCgmu/dokZur/ZfNlYNmrT0nQgAgBKVZfoKAEQd9/nflLz2c+nSf+ikXmf7jgMAQKkYKQcQcxZ8+orsy8eU23WQ1PtG33EAADgmSjmA2LJrtU6ZdY/WJJ0oXfw4FwgCAEQFpq8AiB35OdKrQ5WcmKj26W/IatTynQgAgDJhpBxATHDBoOY9M1xu+7fS1WNlDTibKwAgelDKAcSE2a89ql77PtastjdLHc/zHQcAgHKhlAOIestnz1Cvbx/Vwur91G/YX33HAQCg3CjlAKLazq0b1OD9dG1PaKR2oycrITHRdyQAAMqNUg4gahXm52n7uBtU2x1U7lUvqW6Dxr4jAQBQIZRyAFEra+yd6pK/WEt7/1EnduvvOw4AABVGKQcQlea+P179t01VZqOrlHbZrb7jAABwXCjlAKJO3pZvdfLs+7UiqbN6jn7OdxwAAI4bFw8CEF1y96va68NUWL2W6g95RSnVUn0nAgDguDFSDiBquGBQ2yeNktu9RkkDJ6hJSy4QBACIDZRyAFFjxZt/VZNNH2nVqfdI7c/wHQcAgEpDKQcQHdZ8oU5Ln9CWlj9Rh8vv950GAIBK5a2Um9k4M9tuZkuOWPaYmS03s0VmNt3M6pWw7TozW2xmC8wsK2yhAXixfdMaBV4bIWvYQc2HjZUlMJ4AAIgtPn+zTZB04VHLZkjq6pw7VdJ3kn5dyvbnOOd6OOfSqigfgAiQl5ujPROuV97hQyq49mWpWm3fkQAAqHTeSrlzbqak3Uct+9g5Vxi6O0tSq7AHAxBRFrx4uzoVrtDy/o8ouWln33EAAKgSkfw34JGSPijhMSfpYzOba2bppe3EzNLNLMvMsnbs2FHpIQFUnTlvPaN+O9/UrGaD1evCG33HAQCgykRkKTezByUVSppcwiqnO+d6SbpI0u1mdmZJ+3LOZTjn0pxzaY0bN66CtACqwupFX6vb/N9racqpSrvpH77jAABQpSKulJvZcEmXShrsnHPFreOcyw593i5puqS+4UsIoKrt271D1affqP1WW81umqqk5BTfkQAAqFIRVcrN7EJJ90m6zDmXU8I6Nc2s9ve3JV0gaUlx6wKIPsFAQOvG3KBGwZ3afemLatiUt5YAAGKfz1MiTpX0jaROZrbJzG6S9LSk2pJmhE53+Hxo3RZm9n5o06aSvjKzhZJmS3rPOfehhy8BQBXInPhrdT88W/NPuV+d037sOw4AAGGR5OuJnXODilk8toR1syVdHLq9RlL3KowGwJeVM9R/Q4ay6v1Efa+5x3caAADCxlspB4Ajud1rZW+MkjXtqt43jecCQQCAuMJvPQDe5eYc1PrnrlZBIChd97IspabvSAAAhBWlHIBfzsm9e7faFazWtwMelxq0950IAICwo5QD8GvueFX/9lUFz7xP3c+9zncaAAC8oJQD8GZF1mcqfPdeFZ5wnhLOvt93HAAAvKGUA/Bi9/bNqvfuKG23hjp0ybMSb+wEAMQxfgsCCLvCgnxlj71Bdd1+5VwxXnUbNvUdCQAAryjlAMJuzrhfqmveAi3q8Xt16H667zgAAHhHKQcQVvM/mqgBW15SZsMr1PfKn/mOAwBARKCUAwib9SsWqOPX92lFUif1GP2c7zgAAEQMSjmAsDh0YK/cq0OUb8mqd+NUVUut4TsSAAARg1IOoOo5p40TblLrwCZln/eMmrY60XciAAAiCqUcQNWb9aw67/pE63rco64/usx3GgAAIg6lHECVWjv3Y7mPfyt1vlQnXvGg7zgAAEQkSjmAKpO3Z5Pq/Hu0tia2kK54TjLzHQkAgIhEKQdQNQrzVe3NkaqXlK/8qydKqXV8JwIAIGJRygFUid1v/UramKnEK55R25N7+44DAEBEo5QDqHRz3nleDZaM14ZOI6SuV/mOAwBAxKOUA6hUa5Zkquvc3+rblG5qfvUjvuMAABAVKOUAKs2+PTuV8sYwHbBaajJyipJTqvmOBABAVKCUA6gUwUBAazOGqGlwh3ZfnKFGzdr4jgQAQNSglAOoFJkvPageh7/RvJPvVee+5/uOAwBAVKGUAzhui754U/3WPa+sOuep78D7fMcBACDqUMoBHJfsdSvU5vOfaX1iW3VJHydL4GUFAIDySvIdAEAUK8hV/XdHKiCn5Bsmq0atur4TAQAQlSjlACru/XtUfecSBa+bqloduvpOAwBA1OLvzAAqJPONJ6X5Lyv4o3uUcPLFvuMAABDVKOUAym/zXPVe8hctSu0td/avfacBACDqMX0FQPkc2iW9OkxJdZqr803TlJjEywgAAMeLkXIAZRYoLNR3zw5U8NAO6bqXlFKnke9IAADEBEo5gDKbPf6XOulQluae8qDUoqfvOAAAxAxKOYAymf/xJA3YPEGzG1yqPlfd6TsOAAAxhVIO4Jg2rlyoDv+9RyuTOqp7eobvOAAAxBxKOYBS5Rzcp8DUISq0JNUeNkXVUmv6jgQAQMyhlAMokQsG9e0LI9Q6sFGbz31azdqc5DsSAAAxiVIOoESzX/2r0g58qtkn3KauZ17hOw4AADGLUg6gWNsWf6Zey/+u+TVOU78hf/IdBwCAmEYpB/C/DmxV049uUX7tVjoxfZISEhN9JwIAIKZxKT4AP1CQn6eCyUNUI++Aao6aLtVr6DsSAAAxj5FyAD+wZMKdqrF1jnae+7jU9BTfcQAAiAuUcgD/3+LX1TN7qla0G6JGAwb7TgMAQNyglAOQJG1bNU/unZ9Jrfur09B/+I4DAEBcoZQD0P69u5Q/ebD2B1OlgROlxGTfkQAAiCuUciDOBQMBrc4YoubBrdp8/nNS7Wa+IwEAEHco5UCcy5z0W/XM+VpZne5Wl/4X+o4DAEBcopQDcWzxzOnqu+ZZza19rvpd/6DvOAAAxC1KORCntm5YqVaf/UwbE1urc/p4WQIvBwAA+MJvYSAO5R4+pAMvDVKyK1TioEmqWbue70gAAMQ1SjkQhxaNuVkdC1dq5emPqXXH7r7jAAAQ9yjlQJwpnDNRfXf/W7NaDFPPC4b6jgMAACQl+Q4ARLL1uw5pzJdr9Nb8bB3KK1TNakm6omcLjT7jBLVtWNN3vPLLnq+kD++Va3+2+gx+0ncaAAAQQikHSvD5iu26bdI8FQSCKgw6SdLBvEK9Mnuj3pi7Wc8O6aVzOjXxnLLs9u7cqsC461S3ekMlXTNWiUn8+AMAECm8TV8xs3Fmtt3MlhyxrIGZzTCzlaHP9UvY9kIzW2Fmq8zs/vClRrxYv+uQbps0T4cLAv9XyL9XGHQ6XBDQbZPmaf2uQ54SllMwIHtzlGoX7tKac5+TajbynQgAABzB55zyCZKOvlLJ/ZI+dc51lPRp6P4PmFmipGckXSSpi6RBZtalaqMi3oz5co0KAsFS1ykIBPXil2vDlOg4/edvqpv9pXTRozqp19m+0wAAgKN4K+XOuZmSdh+1+HJJE0O3J0q6ophN+0pa5Zxb45zLl/RKaDug0rw1P/sHI+S/+yRDv/sk4wfrFAadps/fHO5o5bbwk6nSzMfkegxWSt+RvuMAAIBiRNqk0qbOuS2S5JzbYmbFTdhtKWnjEfc3SepX0g7NLF1SuiS1adOmEqMilh3KK/zB/S7b1xS/Xn5hscsjxaZVS9T+q7u1KvFEtf7Jo6pm5jsSAAAoRjSeErG4VuGKWVb0gHMZzrk051xa48aNqzAWYknNamX7/2rNlEj7f+3/d/jQARVMGaygElRj6FRVq17LdyQAAFCCSCvl28ysuSSFPm8vZp1Nklofcb+VpOwwZEMcuaJnCyUllD6qnJRgurJnyzAlKh8XDGrpCyPUNrBeG856Si3adfIdCQAAlCLSSvk7koaHbg+X9HYx68yR1NHM2ptZiqTrQ9sBlWb0GScoObH0H4/kxASNOqN9mBKVz+xpjyht/wxltrtZp55zje84AADgGHyeEnGqpG8kdTKzTWZ2k6SHJZ1vZislnR+6LzNrYWbvS5JzrlDSHZI+krRM0jTn3FIfXwNiV9uGNfXskF6qnpz4PyPmSQmm6smJenZIr4i8gNDy2TPUa9ljWlC9v/oN+6vvOAAAoAy8TYh1zg0q4aEfF7NutqSLj7j/vqT3qygaIEk6p1MTfXjXGXrxy7VKnGIKBJ1qVUvSlT1batQZ7SOykO/cukEN3k/XtoTGap8+WQmJib4jAQCAMojcd6kBEaBtw5r60xVdpX80kCQt+cNPPCcqRaBAuycMVmt3UNlX/1ut6nOBIAAAokWkzSkHUFGfPKSTchdpdf+/6MRu/X2nAQAA5UApB2LA1q+nSN88LfVNV9eL0n3HAQAA5UQpB6LcttULVOeju5Rdu5t0wV98xwEAABVAKQeiWe5+NXl/lKxaTSVd/7KUlOI7EQAAqADe6AlEKRcMKu/1W5S6e42qD3tb1VtG5jnTAQDAsTFSDkSpzEm/V+qq97T7tAel9mf4jgMAAI4DpRyIQku+ekd9Vv9L82qdpfo//oXvOAAA4DhRyoEos3XjKrX45HZtSmylk9InyhL4MQYAINrx2xyIInm5Odo38QZVc/nSdZNUq05935EAAEAloJQDUWTBmFvVqXCFVgx4RG079fAdBwAAVBJKORAl5rz1tPrtekvfNB+iXhfe6DsOAACoRJRyIAqsWvhfdZv/kJamdFefkU/6jgMAACoZpRyIdDm71fyjdO1PqKNmN01RUjIXCAIAINZw8SAgkgWD0pvpqpm7TUnD31W1pq18JwIAAFWAkXIggs156X5p1QzpokdUrV1/33EAAEAVoZQDEapw+YfqvS5Dc+pdKKWN9B0HAABUIaavAJFo91olvZWuYNOu6jZ8rGTmOxEAAKhCjJQDEebwoQPaOuZaBWVKuO5lpdao5TsSAACoYpRyIIK4YFBLMm5Sk5xVWtrvcalBe9+RAABAGFDKgQgy+/Un1GffR8psO1rdzrnWdxwAABAmlHIgQqzI+lQ9l/5NC6v3Vb/hD/uOAwAAwohSDkSAXds2qf67o7QzoZHajZqkhMRE35EAAEAYUcoBzwoL8rV17A2q4w4o54rxqtuwqe9IAAAgzCjlgGdzxv1Cp+Qv1OKef1CH7qf7jgMAADyglAMeLft0kgZsmaTMhleozxW3+44DAAA8oZQDvuz4Tp0z79O2Ot3UY/RzvtMAAACPKOWABwf371HglcGypFQ1velVVUut4TsSAADwiFIOhJtzWjnmRmnXKuVf+aJUt6XvRAAAwDNKORBu3zyjngf+o0Wd7lJKx3N8pwEAABGAUg6E0f7ln0szfied/FP1vP53vuMAAIAIQSkHwmT75rUqeGW49lRvLV3+rGTmOxIAAIgQlHIgDPLzcrV7/CBVd7na/9NxUmod35EAAEAEoZQDYTB/zG3qXLhMy/o9orYn9/YdBwAARBhKOVDFst55Tv12vqFZTQep98UjfMcBAAARiFIOVKE1SzJ1ytzfaWlKN6WN+qfvOAAAIEJRyoEqsm/PTqW8MUwHrJaajpyqpOQU35EAAECEopQDVSAYCGhtxmA1De7Q7osz1KhZa9+RAABABKOUA1Ug59NH1OPwLM07+V517nu+7zgAACDCJfkOAMScVZ+o1tePquCUa9X36vt8pwEAAFGAkXKgEmWvW6GcqSMUbNJFyZf/U5bAjxgAADg2GgNQWQoOq9qbw1UYKNS2C8dIKTV8JwIAAFGCUg5UBuek9+9Rw/3L5K54Qc1POMV3IgAAEEUo5UAlmP/WU9L8SdKZ96puj8t8xwEAAFGGUg4cpxXzvtApC/6kxalp0tm/9h0HAABEIUo5cBx2b9+suu+M1C6rr9ajJksJib4jAQCAKEQpByooUFio7LE3qL7bp4NXjFe9Rs18RwIAAFGKUg5U0Oxxd6tr3gIt6v5bdexxhu84AAAgilHKgQqY99EkDcieqNkNfqo+V93pOw4AAIhylHKgnDauXKiOX9+j75JOUvf0DN9xAABADKCUA+VQeHi/AlOHqNCSVGfYFFVL5QJBAADg+FHKgXJIevdOtXWbtOncZ9SsTUffcQAAQIyIuFJuZp3MbMERH/vN7K6j1jnbzPYdsc7vPMVFHAns3SwtfVN27m/U7czLfccBAAAxJMl3gKM551ZI6iFJZpYoabOk6cWs+qVz7tIwRkMcy9m/W6l712l7i5+qyem/8B0HAADEmIgr5Uf5saTVzrn1voMgjh3Yquo1Nyi3eh2lDhwjJUTcH5gAAECUi/R2cb2kqSU8NsDMFprZB2Z2SjhDIX4U5Ocp+Oow2YXVVf3NL1WnXkPfkQAAQAyK2FJuZimSLpP0WjEPz5PU1jnXXdK/JL1Vyn7SzSzLzLJ27NhRJVkRu+a+eIcSNmUq/5KnpKZdfMcBAAAxKmJLuaSLJM1zzm07+gHn3H7n3MHQ7fclJZtZo+J24pzLcM6lOefSGjduXLWJEVOy3s1Q/+3TNKvJQKX0GOg7DgAAiGGRXMoHqYSpK2bWzMwsdLuvir6OXWHMhhi3dulsdZnzGy1LPkW9Rz3tOw4AAIhxEflGTzOrIel8STcfsewWSXLOPS/pGkm3mlmhpMOSrnfOOR9ZEXv2792l5NeHKceqq/GIqUpOqeY7EgAAiHERWcqdczmSGh617Pkjbj8tieFLVLpgIKDVGUPUNbhdqy6eqpNbtPUdCQAAxIFInr4ChN3sSb9Vz5yvNa/z3Tq53098xwEAAHGCUg6ELJn5lvqseVZza5+rvtc94DsOAACII5RyQJL2blD7L36ujYmtdfLN42VcIAgAAIRRRM4pB8KqIFeaNkw1kpzqD5mmGrXq+U4EAADiDMOBiHtLx90iZc+XXfm86rY+2XccAAAQhyjliGv7/jtWp2yZrtmtbpQ6X+I7DgAAiFNMX0H8yp6vup/9WnltzlTvYU/4TgMAAOIYpRxxae+OLUp6aZBq1mysateNl5L4UQAAAP4wfQVxJ1BYqA1jByvl8A5tOv8FqWYj35EAAECco5Qj7syecK9OzZ2rBd0eVOtuP/IdBwAAgFKO+LJgxhQN2DROs+tdrD5X3eU7DgAAgCRKOeLIplWLdeJ/79aqxBN1avoYLhAEAAAiBq0EcSHn4D4VTBmigBJVY+hUpdao5TsSAADA/6GUI+a5YFDfZoxU28B6bTj7n2rRrpPvSAAAAD9AKUfMm/Pao0rb/4ky292sU8++2nccAACA/0EpR2zbMEt9VjymlfXPUL9hf/WdBgAAoFiUcsSsAzs3yU0bLqvbWh3TJykhMdF3JAAAgGJRyhGTCvPztDHjehUc2iNdN0mqXs93JAAAgBJRyhGTEj/7g7rkL9ainn+QmnX1HQcAAKBUlHLEnPyFr8tmPSP1TVfaZbf6jgMAAHBMlHLElPXLslQ4/Xbtb9xLuuAvvuMAAACUSZLvAEBlObBvtxKmDVWOUuWuHC8lpfiOBAAAUCaMlCMmuGBQqzKGqnlwq7Zd8Jwat2jnOxIAAECZUcoREzIn/V49D32lrJPu0imnXew7DgAAQLlQyhH1lnz1jvqs/pfm1jpb/Qb91nccAACAcqOUI6pt3bhKLT+5XZsSW6nzzRNlCXxLAwCA6EODQdTKy83R/omDlOwKpOsmqWbter4jAQAAVAilHFFr3/R7dFLhd1p52iNq26mH7zgAAAAVRilHdJo/WU1WTNbBtDvU8yfDfacBAAA4LpRyRJ1VC/+rwL9/IbU/U7Uu+oPvOAAAAMeNUo7okrNb9d+9STuDtZRzWYaUyPWvAABA9KOUI3oEg9Kbo9UgsFPBayeqRv3mvhMBAABUCko5osbyVx+UVn0iu+gRNT/lDN9xAAAAKg2lHFFh4WevqPOKZ7Ww0SVS2kjfcQAAACoVpRwRb/OaZWo/826tTjxBnUZmSGa+IwEAAFQqSjki2uFDB5Q7eZCcpOpDpii1Ri3fkQAAACodpRwRywWDWpIxUu0L12ndWU+pRfuTfUcCAACoEpRyRKzZrz+uPvs+1uy2o9X9nGt9xwEAAKgylHJEpOVZn6rn0oe1sHpf9R3+sO84AAAAVYpSjoizZ/tmNXh3lHYkNFK70VOUkJjoOxIAAECVopQjsgQKVfe9dNW3Q8q9coLqNmjsOxEAAECV4xrliCgFMx5S8vqvlHDF8zrx1NN8xwEAAAgLRsoRMeZ9OEHJs/6lfV2HSz0G+Y4DAAAQNpRyRIYd36n73Ae0NrWLqv/0Ud9pAAAAworpK/CuMGefkl4dosTk6mp/8+tStVTfkQAAAMKKkXJ45YJBLXp2iII7V8pdM06q29J3JAAAgLCjlMOrzCl/VK+DMzX7xJ/JTjjLdxwAAAAvKOXwZul/31Payqc0r+aZ6jf4Id9xAAAAvKGUw4ttm9eo2YxbtTmxhU66+SVZAt+KAAAgftGEEHb5ebnaM/4Gpbo8aeDLqlWnvu9IAAAAXlHKEXbzx9yqzoXLtLzfw2rbuZfvOAAAAN5RyhFWc95+Vv12vqlZTQep98UjfMcBAACICBFZys1snZktNrMFZpZVzONmZv80s1VmtsjMGG6NAoHsReo2/yEtTTlVaaP+6TsOAABAxIjkiwed45zbWcJjF0nqGProJ+m50GdEqsN7lPjaUFmt+mo+dIqSklN8JwIAAIgYETlSXgaXS3rJFZklqZ6ZNfcdCsULBgLaOHaY3L7NSrjuZTVo2tp3JAAAgIgSqaXcSfrYzOaaWXoxj7eUtPGI+5tCyxCBNr3zJ7XeOVOLu94nte7rOw4AAEDEidTpK6c757LNrImkGWa23Dk384jHrZhtXHE7CpX674t9npktqeSsKJtG0q92Sr/ynSNeNZJU0nQwVD2Ovz8ce784/n5x/P3pVN4NIrKUO+eyQ5+3m9l0SX0lHVnKN0k6cg5EK0nZJewrQ1KGJJlZlnMurUpCo1Qce784/n5x/P3h2PvF8feL4+9PcScqOZaIm75iZjXNrPb3tyVdIOno0e13JA0LnYWlv6R9zrktYY4KAAAAVIpIHClvKmm6mUlF+aY45z40s1skyTn3vKT3JV0saZWkHEmc8BoAAABRK+JKuXNujaTuxSx//ojbTtLtFdh9xnFEw/Hh2PvF8feL4+8Px94vjr9fHH9/yn3srajfAgAAAPAl4uaUAwAAAPEmJku5mbU2s8/NbJmZLTWzO0PLHzKzzWa2IPRxse+ssaik4x967GdmtiK0/FGfOWNRKd/7rx7xfb/OzBZ4jhqTSjn+PcxsVuj4Z5kZJ+yvAqUc/+5m9o2ZLTazf5tZHd9ZY42ZpZrZbDNbGDr2fwgtb2BmM8xsZehzfd9ZY1Epx//a0P2gmXEWlipSyvF/zMyWm9kiM5tuZvVK3U8sTl8JXd2zuXNuXuhMLnMlXSFpoKSDzrnHfeaLdaUc/6aSHpR0iXMuz8yaOOe2e4wac0o69s65b49Y5wkVnbHoj75yxqpSvvf/IelJ59wHocGAXznnzvYWNEaVcvwnSrrHOfeFmY2U1N4591uPUWOOFZ2doaZz7qCZJUv6StKdkq6StNs597CZ3S+pvnPuPp9ZY1Epx3+fpKCkF1T0M1Du0/Th2Eo5/nUkfeacKzSzRySptO//mBwpd85tcc7NC90+IGmZuOJn2JRy/G+V9LBzLi/0GIW8kh3rez/0wjFQ0lQ/CWNbKcffqejFWZLqqoTrKuD4lHL8O+n/X+tihqSr/SSMXa7IwdDd5NCHk3S5iv5TpNDnK8KfLvaVdPydc8uccys8RosLpRz/j51zhaHls1R0XZ0SxWQpP5KZtZPUU1JmaNEdoT8jjOPPaFXvqON/kqQzzCzTzL4wsz5ew8W4Yr73JekMSduccyu9hIojRx3/uyQ9ZmYbJT0u6df+ksWHo47/EkmXhR66Vj+8+BwqiZklhqbGbZc0wzmXKanp99cRCX1u4jFiTCvh+CNMynD8R0r6oLR9xHQpN7Nakt6QdJdzbr+k5ySdKKmHpC2SnvCXLvYVc/yTJNWX1F/SvZKmhUZuUcmKOfbfGyRGyatcMcf/Vkm/cM61lvQLSWN95ot1xRz/kZJuN7O5kmpLyveZL1Y55wLOuR4qGg3sa2ZdPUeKKxx/v0o7/mb2oKRCSZNL20fMlvLQnJ43JE12zr0pSc65baGDFpQ0RhJvtqoixR1/SZskvRn6M89sFc1za+QrY6wq4djLzJJUNL/zVV/Z4kEJx3+4pO9vvyZee6pMCa/9y51zFzjneqvoP6WrfWaMdc65vZL+I+lCSdtCc/2/n/PPtMUqdtTxR5gdffzNbLikSyUNdsd4I2dMlvLQ6OtYScucc38/YnnzI1a7UkV/0kQlK+n4S3pL0rmhdU6SlCJpZ9gDxrBSjr0knSdpuXNuU/iTxYdSjn+2pLNCt8+VxPShKlDKa3+T0OcESb+R9Hzxe0BFmVnj788sYWbVFXq9kfSOiv5TqtDnt70EjHGlHH+EQUnH38wulHSfpMuccznH3E+Mnn3lR5K+lLRYRaOxkvSAiv5030NFbz5ZJ+nm7+e6ofKUcvw/kTRORf8G+Sp6J/hnPjLGqpKOvXPufTObIGnWkVfHReUq5Xt/v6SnVDSFK1fSbc65uV5CxrBSjn9H/f+rQL8p6dfHGrFC+ZjZqSp6I2eiigb8pjnn/mhmDSVNk9RG0gZJ1zrndvtLGptKOf5XSvqXpMaS9kpa4Jz7ibegMaqU479KUjVJu0KrznLO3VLifnhdAgAAAPyKyekrAAAAQDShlAMAAACeUcoBAAAAzyjlAAAAgGeUcgAAAMAzSjkAAADgGaUcAMLEzNqZmTvGxzozu7EM600I7XNCGda9MbTuujKs266sOT0dw2Pl4jy/AKISpRwAwmuzc86K+5DU/oj1Jpey3oij9jm6lHUnHrVux1LWXV+BnD4kl5INAKISpRwAAADwjFIOADhuZtbMzP5pZmvMLM/MtpvZDDM7/4h1/hOaQtPOzKab2V4z2xOaglPLzBLM7AEzW2tmuWY2z8xO9/l1AUC4JPkOAACIbmbWTtJ/JTWV9JKkLEk1JfWXdJ6kGUesXlPSZ5JmSrpfUh9JIyWlStolqZ+kf0lKlnSPpH+bWVvn3IFwfC0A4AulHABwvJ6V1ELShc65j458wMyO/otsI0mPOuceC91/3szqSxooaZ6kAc65gtC2yyS9LekGSS9UYX4A8I7pKwCACjOzBpIulPTh0YVckpxzwaMWBVQ0En6kLyWZpOe/L+RHLJekjpUUFwAiFqUcAHA8OqioUM8v4/pbnHO5Ry3bE/q89siFzrnvlzeseDwAiA6UcgDA8fj+NIRlPT94oAKPcapDADGPUg4AOB4rVVTIe/oOAgDRjFIOAKgw59xuSR9IusjMzjv6cTNjlBsAyoCzrwAAjtcdkr6W9IGZTZQ0V1J1FZ3ecJ2k+/xFA4DoQCkHABwX59xaM0uT9FtJF0sapqI3by6UlOEzGwBEC0o5AOC4Oec2S7rlGOucXcLyCZImlPAY018AxAXmlAMAAACeUcoBILxampkr7kM/PE/34FLWG3/UPseUsu7wo9ZdWcq6bSuQ04eCUrIBQFQy53gNAwAAAHxipBwAAADwjFIOAAAAeEYpBwAAADyjlAMAAACeUcoBAAAAzyjlAAAAgGeUcgAAAMCz/wcpsQ+OJ08ehAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,9))\n",
    "plt.xlim(25,32)\n",
    "plt.ylim(5,25)\n",
    "plt.scatter(x,y,100)\n",
    "plt.xlabel('弹簧长度（cm）',fontsize=18)\n",
    "plt.ylabel('砝码重量（g）',fontsize=18)\n",
    "plt.title('胡克定律实验',fontsize=20)\n",
    "# plt.plot((x[0],x[7]),(y[0],y[7]),color='r')\n",
    "# plt.plot((x[1],x[6]),(y[1],y[6]),color='b')\n",
    "plt.plot(x1,y1, linestyle='-.')\n",
    "plt.plot(x1,y1)\n",
    "plt.plot((26.51,26.51),(10,5*26.51-125),color='r')\n",
    "plt.plot((29.33,29.33),(20,5*29.33-125),color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 最小二乘法求出k,b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def least_square(x, y):\n",
    "    if len(x) == 2:\n",
    "        # 此时x为自然序列\n",
    "        sx = 0.5 * (x[1] - x[0] + 1) * (x[1] + x[0])\n",
    "        ex = sx / (x[1] - x[0] + 1)\n",
    "        sx2 = ((x[1] * (x[1] + 1) * (2 * x[1] + 1))\n",
    "               - (x[0] * (x[0] - 1) * (2 * x[0] - 1))) / 6\n",
    "        x = np.array(range(x[0], x[1] + 1))\n",
    "    else:\n",
    "        sx = sum(x)\n",
    "        ex = sx / len(x)\n",
    "        sx2 = sum(x ** 2)\n",
    "\n",
    "    sxy = sum(x * y)\n",
    "    ey = np.mean(y)\n",
    "\n",
    "    a = (sxy - ey * sx) / (sx2 - ex * sx)\n",
    "    b = (ey * sx2 - sxy * ex) / (sx2 - ex * sx)\n",
    "    return a, b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x = np.array(x)\n",
    "train_y = np.array(y)\n",
    "k,b = least_square(train_x,train_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 显示k，b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.768895049688474, -117.95038648113349)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k,b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 当弹簧为50cm时，测量重物的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120.49436600329021"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=50\n",
    "y=k*x+b\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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